cnPredictionPanel: Plot bivariate normal prediction regions on a lattice panel

In rscharpf/crlmmCompendium: This package contains the compendium for copy number estimation with the crlmm package

Description

Bivariate normal prediction regions are added to a lattice panel.

Usage

cnPredictionPanel(x, y, ..., object, copynumber, x.axis, line.col,
line.lwd, shades, subscripts, data.last =FALSE, highlight.index = NULL, scale.sd = rep(1, 2))

Arguments

x	passed to panel.xyplot
y	passed to panel.xyplot
...	passed to panel.xyplot
object	object of class CNSet
copynumber	Integer vector. Which prediction regions to plot.
x.axis	valid entries are 'A' or 'B', indicating whether the A or B allele is to be plotted on the x-axis.
line.col	the color for the border of the prediction regions
line.lwd	currently ignored
shades	the fill color for the prediction regions
subscripts	passed to panel.xyplot
data.last	whether to plot the data before or after plotting the prediction regions
highlight.index	an integer vector. If not missing, this vector can be used to highlight the position of some samples in the A vs B scatterplot. These samples will be plotted with an 'X' instead of an 'o'.
scale.sd	whether to scale the A and B standard deviations to provide a sample specific prediction region. This is experimental and defaults to 1 (no scaling)

Value

a lattice object

Author(s)

R. Scharpf

See Also

panel.xyplot, CNSet-class

Examples

library("crlmm")
library("crlmmCompendium")


###################################################
### chunk number 27: loadObject-exampleData1
###################################################
#line 1164 "manuscript.Rnw"
data(exampleData1)


###################################################
### chunk number 30: defineLatticeObjects
###################################################
#line 1223 "manuscript.Rnw"
ldat <- prePredictPanel(exampleData1)
shades <- makeTransparent(brewer.pal(6, "BrBG"), alpha=0.6)[c(1,2,3,5,6)]
##replace the middle color (white) with something else
mykey <- simpleKey(as.character(0:4), points=FALSE, rectangles=TRUE, col="black", space="right", cex=0.7)
mykey$rectangles[["col"]] <- shades
fig2 <- xyplot(A~B|snp, ldat, cex=0.3, panel=cnPredictionPanel, object=exampleData1,
	       x.axis="B", copynumber=0:4, line.col=shades, line.lwd=1.5,
	       shades=shades, ylab=expression(log[2](I[A])), xlab=expression(log[2](I[B])),
	       par.strip.text=list(lines=0.9, cex=0.6),
	       key=mykey)


###################################################
### chunk number 31: ABscatterplots
###################################################
#line 1237 "manuscript.Rnw"
pars <- trellis.par.get()
pars$axis.text$cex <- 0.3
pars$xlab.text$cex <- 0.8
trellis.par.set("axis.text", pars$axis.text)
trellis.par.set("axis.text", pars$xlab.text)
print(fig2)

rscharpf/crlmmCompendium documentation built on May 28, 2019, 3:31 a.m.